MP03 - New York City’s Trees: Distribution, Diversity, and Urban Impact

Author

Tova Hirschhorn

Published

November 14, 2025

I. Executive Summary

Mini-Project 03 explores New York City’s extensive green spaces, encompassing over 30,000 acres of public parkland across its 51 City Council districts in the five boroughs.

The project emphasizes the responsible acquisition of data from the NYC TreeMap and the NYC Department of Planning, leveraging API access, big data techniques, and geospatial analysis to ensure data integrity and reproducibility.

The analysis integrates multiple spatial data sources to examine tree distribution, species diversity, and overall tree health, while making use of visualization techniques to clearly convey patterns and insights. This approach provides a deeper understanding of the environmental space around us and highlights the community value of New York’s urban forest.

The project also includes a government project design component, using district-level tree data to make an informed hypothetical Parks Department proposal.

II. Data Acquisition and Preparation

Two primary datasets were acquired and prepared: NYC City Council District Boundaries and NYC Tree Points.

City Council District Boundaries: The file was downloaded as a static file from the NYC Department of Planning site. The data was stored locally as a zip file, then unzipped, and read using the st_read function. The data was then transformed to the World Geodetic System (WGS 84) coordinate system to standardize projections and integration with other geospatial data.
NYC Tree Points: The complete NYC TreeMap dataset was obtainted from the NYC OpenData API in GeoJSON format. The data was downloaded iteratively using $limit and $offset parameters to ensure responsible API usage and the results were saved locally to prevent repeated downloads. All files were then combined into a single sf object using bind_rows. Subsetting and caching techniques were used to handle large datasets efficiently.

Code

#Task 1 - Downloading City Council Districts data
#Create directory
if(!dir.exists(file.path("data", "mp03"))){
    dir.create(file.path("data", "mp03"), showWarnings=FALSE, recursive=TRUE)
}

#Define paths and URL
NYC_COUNCIL_ZIP <- file.path("data", "mp03", "nycc_25c.zip")
NYC_COUNCIL_URL <- "https://s-media.nyc.gov/agencies/dcp/assets/files/zip/data-tools/bytes/city-council/nycc_25c.zip"

#Download ZIP only if file not not exist
if(!file.exists(NYC_COUNCIL_ZIP)) {
  download.file(NYC_COUNCIL_URL, destfile = NYC_COUNCIL_ZIP, mode = "wb")
  message("Downloaded NYC City Council Districts ZIP file.")
} else {
  message("ZIP file already exists; skipping download.")
}

#Define shapefile path
NYC_COUNCIL_SHP <- file.path("data", "mp03", "nycc.shp")

#Unzip file only if shapefile does not exist
if (!file.exists(NYC_COUNCIL_SHP)) {
  unzip(NYC_COUNCIL_ZIP, exdir = "data/mp03")
  message("Unzipped shapefile.")
} else {
  message("Shapefile already exists; skipping unzip.")
}

#Correct path to shapefile
NYC_COUNCIL_SHP <- "data/mp03/nycc_25c/nycc.shp"

#Read shapefile
council_districts <- st_read("data/mp03/nycc_25c/nycc.shp", quiet = TRUE)

#Check first few rows
#head(council_districts)

#Transform to WGS84
council_districts <- st_transform(council_districts, crs = "WGS84")

#Simply geometry
council_districts <- council_districts |>
  mutate(geometry = st_simplify(geometry, dTolerance = 10))

Code

#Task 2 - Downloading NYC Open Data Forestry Tree Points data
#Downloading Tree Points using API
#Create folder to store data if it doesn't exist
if(!dir.exists("data/mp03")) dir.create("data/mp03", recursive = TRUE)

#Define API endpoint and file paths
base_url <- "https://data.cityofnewyork.us/resource/hn5i-inap.geojson"
limit <- 50000 #number of rows per request
offset <- 0   #start from the beginning
page <-1 #page counter

#List to store each page
all_data<-list()

#Loop to download all pages
repeat {
  file_path <- file.path("data/mp03", paste0("trees_", page, ".geojson"))
  
  if(!file.exists(file_path)) {
    # Build request with limit and offset
    req <- request(base_url) |>
      req_url_query(`$limit` = limit, `$offset` = offset)

  #Perform the request    
    resp <-req_perform(req)

  #Save raw response to a file
  writeBin(resp$body, file_path)
    message("Downloaded page ", page)
  } else {
    message("File already exists: page ", page, "; skipping download")
  }

  #Read the downloaded GeoJSON
  page_data <- st_read(file_path, quiet=TRUE)
  all_data[[page]] <- page_data

  # If fewer rows returned than limit, we reached the end
  if(nrow(page_data) < limit) break

  #Increment for next page
  page<- page+1
  offset <- offset + limit
}

#Combine all pages into a single sf object
nyc_trees <- bind_rows(all_data)

#read the first page of tree points
nyc_trees_page1 <- st_read("data/mp03/trees_1.geojson", quiet = TRUE)

#Creating a smaller sample for plotting
#set.seed(123) # reproducibility
#nyc_trees_sample <- nyc_trees_page1 |> slice_sample(n = 500000)

III. Exploring New York City’s Urban Tree Environment

New York City, divided into 51 City Council Districts, is home to over one million trees. As the map below shows, even in a city defined by towering skyscrapers, greenery plays a vital role, weaving pockets of nature throughout the urban landscape.

Code

#Task 3 - Mapping NYC trees
ggplot()+
#Creating a ggplot for Council Districts
  geom_sf(data=council_districts, 
        fill="gray95",
        color= "black",
        size=6) +
#Create ggplot for NYC Trees
  geom_sf(data=nyc_trees,
        color="forestgreen",
        alpha=0.01,
        size=0.2) +
  theme_minimal() +

  labs(
    title = "New York City Trees by City Council District",
    subtitle= paste(scales::comma(nrow(nyc_trees)), "Trees Across 51 Districts"),
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),      # center & bold title
    plot.subtitle = element_text(hjust = 0.5, size = 12),                 # center subtitle
    plot.caption = element_text(size = 8),
    plot.margin = margin(10, 10, 10, 10)                                
  )

Code

#Task 4 - Creating District-Level joins for analysis of tree coverage
#Assigning each tree to the district that contains it
trees_with_district <- st_join(
  nyc_trees,
  council_districts,
  join=st_intersects
)

#Check first few rows
#head(trees_with_district)

Q1. Council City District 51, which covers the South Shore of Staten Island, has the most trees in the city. The district features numerous parks, including Great Kills Park, Blue Heron Park, Wolfe’s Pond Park, Long Pond Park. It is also home to Freshkills Park, currently under development on top of a former landfill. Once completed, Freshkills Park will cover 2,200 acres, making it the largest park created in New York City since the 19th Century.

Code

#Task 4. Q1 - Determine which council district has the most trees
trees_by_district <- trees_with_district |>
  st_set_geometry(NULL) |> 
  group_by(CounDist)|>
  summarise(num_trees = n())|>
  arrange(desc(num_trees))

#Select top 10 districts by number of trees
top_trees <- trees_by_district |> slice_max(num_trees, n=10)

#Create bar chart
ggplot(top_trees, aes(x = reorder(CounDist, -num_trees), y = num_trees)) +
  geom_col(fill = "forestgreen") +
  labs(
    title = "Top 10 NYC Council Districts by Number of Trees",
    x = "Council District",
    y = "Number of Trees"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
    plot.title = element_text(hjust = 0.5, face = "bold")
  ) +
  scale_y_continuous(labels = scales::comma)

Q2. New York City’s 7th City Council district is relatively small compared to other districts, covering roughly 5.5 km² of land. Despite its size, it has the highest tree density in the city, with approximately 2.8 trees per hectare. This district covers several small neighborhoods in upper Manhattan, such as Hamilton Heights, Morningside Heights, Manhattanville, and Manhattan Valley. It also includes parts of Washington Heights and the Upper West Side.

Code

#Task 4. Q2 - Determine which council district has the highest density of trees.
#Count trees per district
tree_counts <- trees_with_district|>
  group_by(CounDist, Shape_Area) |>
  summarise(tree_count = n(), .groups = "drop")

#Compute tree density
tree_counts <- tree_counts |>
  mutate(trees_density = tree_count/Shape_Area)

#Find the district with the highest density
highest_density <- tree_counts |>
  filter(trees_density == max(trees_density, na.rm = TRUE))
highest_density

Q3. The choropleth map below highlights the New York City districts with the highest fractions of dead trees. District 32, covering neighborhoods such as Howard Beach, Ozone Park, and the Rockaways shows the most severe conditions, with 14.5% of its trees classified as dead. Other highly affected districts are concentrated in neighborhoods on Staten Island, other parts of Queens, and Brooklyn, where dead-tree rates exceed 14%.

Code

#Task 4. Q3 - Determine which council district has the highest fraction of dead trees out of all trees.
library(sf)
library(dplyr)
library(tidyverse)
library(scales)

#Filter out uknown and NA trees
trees_clean <- trees_with_district|>
  filter(!is.na(tpcondition) & tpcondition != "Unknown")

#Compute fraction of dead trees per district
dead_tree_fraction <- trees_clean|>
  st_set_geometry(NULL)|>
  group_by(CounDist) |>
  summarise(
    total_trees = n(),
    dead_trees = sum(tpcondition == "Dead"),
    dead_fraction = dead_trees/total_trees
            )|>
  arrange(desc(dead_fraction))

#Joining fraction back to district geomtries to create choropleth map
districts_with_fraction <- council_districts|>
  left_join(dead_tree_fraction, by = "CounDist")
  
ggplot(districts_with_fraction) +
  geom_sf(aes(fill = dead_fraction), color = "gray40", size = 0.5) +
  scale_fill_viridis_c(
    option = "C",
    direction = -1,
    trans = "sqrt",   # exaggerates small differences
    labels = scales::percent_format(accuracy = 1)
  ) +
  labs(
    title = "Fraction of Dead Trees by Council District",
    fill = "Dead Tree Fraction",
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    plot.caption = element_text(size = 8),
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "white", color = NA),
    panel.grid.major = element_line(color = "gray90", size = 0.3),
    panel.grid.minor = element_line(color = "gray95", size = 0.2),
    plot.margin = margin(10, 10, 10, 10)
  )

Q4. Manhattan is home to a diverse array of tree species across the borough. The chart below highlights the top five species in Manhattan, with the Thornless Honeylocust being the most common. This fast-growing tree tolerates pollution and adapts to various soil types. It thrives in the sun and in the Fall, its leaves turn yellow and often shrivel away, which reduces the need for raking.

Code

#Task 4. Q4 - Determine what is the most common tree species in Manhattan
#Adding a borough column
trees_with_district <- trees_with_district |>
  mutate(
    Borough = case_when(
      CounDist >= 1 & CounDist <= 10  ~ "Manhattan",
      CounDist >= 11 & CounDist <= 18 ~ "Bronx",
      CounDist >= 19 & CounDist <= 32 ~ "Queens",
      CounDist >= 33 & CounDist <= 48 ~ "Brooklyn",
      CounDist >= 49 & CounDist <= 51 ~ "Staten Island",
      TRUE ~ NA_character_
    )
  )

#Filter for Manhattan
manhattan_trees <- trees_with_district |>
  filter(Borough == "Manhattan")

#Count tree species and find the most common for Manhattan
most_common_manhattan <- manhattan_trees |>
  st_set_geometry(NULL) |>  # remove geometry for speed
  group_by(genusspecies) |>
  summarise(num_trees = n(), .groups = "drop") |>
  arrange(desc(num_trees)) |>
  slice(1)  # top species

#Filter top 10 species in Manhattan
top_species <- manhattan_trees |>
  st_set_geometry(NULL) |>
  group_by(genusspecies) |>
  summarise(num_trees = n(), .groups = "drop") |>
  arrange(desc(num_trees)) |>
  slice_head(n = 5)

# Wrap species names to ~20 characters per line
top_species <- top_species|>
  mutate(genusspecies_wrapped = str_wrap(genusspecies, width = 20))

#Plotting the results
ggplot(top_species, aes(x = reorder(genusspecies_wrapped, num_trees), y = num_trees, fill = num_trees)) +
  geom_col(width = 0.7, show.legend = TRUE) +
  coord_flip() +
  scale_y_continuous(labels = scales::comma, expand = c(0,0)) +
  scale_fill_gradient(
    low = "lightgreen",
    high = "darkgreen",
    labels = scales::comma  # add commas to legend labels
  ) +
  labs(
    title = "Manhattan's 5 Most Common Tree Species",
    x = NULL,
    y = "Number of Trees",
    fill = "Number of Trees",
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    plot.caption = element_text(size = 8, hjust = 1),
    axis.text.y = element_text(size = 9),
    axis.text.x = element_text(size = 9),
    axis.title.x = element_text(size = 10),  
    legend.title = element_text(size = 10), 
    panel.grid.major = element_blank(),  # remove all major grids
    panel.grid.minor = element_blank(),  # remove all minor grids
    plot.margin = margin(t = 10, r = 10, b = 10, l = 60)
  )

Q5. Since the Thornless Honeylocust is the most common tree species in Manhattan, it is no surprise that it would be the closest tree to Baruch College. This species is widely used in urban and suburban landscaping, often planted along streets and in parking lots due to its remarkable adaptability to urban stress.

Code

#Task 4. Q5 - Determine the tree species closest to Baruch's campus
#Creating Baruch point - coordinate lat=40.7394, lon=-73.9833
baruch_point <- st_sfc(st_point(c(-73.9833, 40.7394)), crs = 4326)

# Project to NY State Plane (feet)
trees_proj <- st_transform(trees_with_district, 2263)
baruch_proj <- st_transform(baruch_point, 2263)

# Compute distances in feet
trees_proj <- trees_proj |>
  mutate(distance_to_baruch_ft = as.numeric(st_distance(geometry, baruch_proj)))

# Find the closest tree
closest_tree <- trees_proj |>
  arrange(distance_to_baruch_ft) |>
  slice(1)

closest_tree$genusspecies     
closest_tree$distance_to_baruch_ft

IV. Government Project Design

New York City Council District 1 encompasses several diverse and historically significant neighborhoods in Lower Manhattan, including the Financial District, Battery Park City, Chinatown, Tribeca, SoHo, and the Lower East Side. The district has experienced rapid development and land-use changes in recent years. The city’s rezoning efforts for new affordable housing has also placed pressure on existing green spaces. The latest debate on the future of the Elizabeth Street Garden highlights community concerns about the loss of accessible, high-quality green space. These changes underscores the urgent need to protect and expand the district’s urban forest.

To preserve environmental quality, improve public health, and prevent further loss of green areas, this proposal establishes the District 1 Tree Restoration and Expansion Initiative. This plan aims to strengthen the district’s urban forestry by improving tree health, expanding tree coverage, and strengthening the district’s green infrastructure.

This initiative focuses on strengthening the district’s urban forest by:

Replacing 500 unhealthy trees currently classified as “poor”, “critical”, or “dead”.
Planting 1000 new trees to expand coverage, increase the district’s biodiversity, and improve air quality.
Enhancing maintenance of tree health through regular pruning and watering, and targeted care for trees in unhealthy condition.
Promoting community engagement through monthly tree-related educational and recreational events to build environmental awareness.

New York City’s District 1 envisions a greener, more resilient landscape, where all residents have equitable access to high-quality green space. It aims to create an urban refuge that supports biodiversity, improves quality of life, and provides relief from the intensity of city living.

The following analysis provides the foundation for the District 1 Tree Restoration and Expansion Initiative, supporting the targeted needs in tree replacement, planting and maintenance.

TREE POPULATION SUMMARY
District 1 is home to 12,268 trees, placing it near the middle of the distribution among other Manhattan districts. Although it has more trees than districts with the fewest trees, such as District 5, which spans New York’s Upper East Side, Roosevelt Island, and a small part of East Harlem, its tree density is lower than some of the other higher-ranking districts.

Code

#How many trees in D1?
trees_in_d1 <- trees_with_district |>
  filter(CounDist == 1) |>
  nrow()

trees_in_d1

#Count trees by district on for Manhattan
trees_manhattan_counts <- trees_with_district|>
  filter(Borough=="Manhattan")|>
  st_set_geometry(NULL)|>
  count(CounDist, name = "num_trees")|>
  arrange(desc(num_trees))

trees_manhattan_counts

trees_manhattan_counts <- trees_manhattan_counts %>%
  mutate(
    highlight = ifelse(CounDist == 1, "District 1", "Other Districts"),
    CounDist = factor(CounDist)  # make it a factor for plotting
  )

# Plot
ggplot(trees_manhattan_counts,
       aes(x = reorder(CounDist, num_trees),
           y = num_trees,
           fill = highlight)) +
  geom_col() +
  coord_flip() +
  scale_y_continuous(labels = scales::comma) +
  scale_fill_manual(values = c("District 1" = "darkgreen",
                               "Other Districts" = "lightgreen")) +
  labs(
    title = "Number of Street Trees by Manhattan Council District",
    x = "Council District",
    y = "Number of Trees",
    fill = "",
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.text.x = element_text(size = 8),  
    axis.text.y = element_text(size = 8), 
    legend.position = "none",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )

TREE DENSITY AND DISTRIBUTION
Compared to other Manhattan districts, District 1 is the third largest area, covering nearly 7.23 km² of land. Despite its size, its tree density is among the lowest in the borough, with less than 2 trees per hectare. This highlights the pressing need to expand green space and increase tree coverage in the district.

Code

#Calculate tree density for more accurate comparison of number of trees per unit area
#Calculate land area for all districts in Manhattan
manhattan_districts_area <- council_districts |>
  filter(CounDist >= 1 & CounDist <= 10) |>
  mutate(
    area_sqm = st_area(geometry),           
    area_km2 = as.numeric(area_sqm) / 1e6
  ) |>
  select(CounDist, area_sqm, area_km2)|>
  arrange(desc(area_km2))

#manhattan_districts_area

# Filter for Manhattan
manhattan_trees <- trees_with_district |> 
  filter(Borough == "Manhattan")

# Count tree density per district
trees_manhattan_density <- manhattan_trees |> 
  st_set_geometry(NULL) |>  # remove geometry for faster processing
  group_by(CounDist, Shape_Area) |> 
  summarise(num_trees = n(), .groups = "drop") |> 
  mutate(tree_density_per_sqm = num_trees / Shape_Area,   # trees per m^2
         tree_density_per_hectare = tree_density_per_sqm * 10000) |>  # trees per hectare
  arrange(desc(tree_density_per_hectare))  # optional: sort by density

# View the results
trees_manhattan_density

#Create visual plot of tree density in Manhattan
manhattan_districts <- council_districts |> 
  filter(CounDist >= 1 & CounDist <= 10)

# Compute number of trees per district
trees_manhattan <- trees_with_district |> 
  filter(Borough == "Manhattan") |> 
  st_set_geometry(NULL) |> 
  group_by(CounDist) |> 
  summarise(num_trees = n(), .groups = "drop")

#Convert both to integer
manhattan_districts <- manhattan_districts |>
  mutate(CounDist = as.integer(CounDist))

trees_manhattan_counts <- trees_manhattan_counts |>
  mutate(CounDist = as.integer(CounDist))

# Combine tree counts with district geometries
manhattan_districts <- manhattan_districts |> 
  left_join(trees_manhattan_counts, by = "CounDist") |> 
  mutate(
    tree_density_per_sqm = num_trees / Shape_Area,
    tree_density_per_hectare = tree_density_per_sqm * 10000
  )

# Plot choropleth map
ggplot(manhattan_districts) +
  geom_sf(aes(fill = tree_density_per_hectare), color = "black", size = 0.3) +
  geom_sf_text(aes(label = CounDist), size = 4, color = "white") +
  scale_fill_viridis(
    option = "J",
    direction = -1,
    name = "Trees per hectare"
  ) +
  labs(
    title = "Tree Density by Manhattan City Council District",
    subtitle = "Number of trees per hectare of total district area",
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    plot.caption = element_text(size = 8, hjust = 1),
    plot.margin = margin(20, 20, 20, 20),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    plot.background = element_blank(),
    axis.text = element_blank(),    # remove axis text
    axis.ticks = element_blank(),   # remove ticks
    axis.title = element_blank()    # remove axis titles
  )

TREE HEALTH PROFILE
The tree health in District 1 is of significant concern. Over 15% of its trees are classified as being in “poor”, “critical”, or “dead” condition, making it the third highest district in Manhattan with unhealthy trees. Although District 10 has the highest percentage (18%) of trees in poor condition, it also has a substantially larger total number of trees. In contrast, District 1’s combination of lower tree density coupled with a relatively high proportion of unhealthy trees highlights the urgency for targeted care, maintenance, and other efforts to improve the health of the district’s urban forest.

Code

#Define condition order from best to worst
tp_levels <- c("Excellent", "Good", "Fair", "Poor", "Critical", "Dead")

#Filter out unknown and NA conditions and prepare data in ordered factor
trees_health_manhattan <- manhattan_trees |>
  filter(!is.na(tpcondition) & tpcondition != "Unknown") |>
  st_set_geometry(NULL) |>
  mutate(tpcondition = factor(tpcondition, levels = tp_levels, ordered = TRUE),
         highlight = if_else(CounDist == 1, "District 1", "Other Districts")) |>
  group_by(CounDist, tpcondition) |>
  summarise(num_trees = n(), .groups = "drop")

# Plot stacked bar chart for all tpcondition
ggplot(trees_health_manhattan, aes(x = factor(CounDist), y = num_trees, fill = tpcondition)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Greens") +
  labs(
    title = "Tree Health by Manhattan City Council District",
    x = "Manhattan Council District",
    y = "Number of Trees",
    fill = "Tree Condition",
    caption = "Data: NYC Open Data and NYC Planning"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    axis.text.x = element_text(size = 8),
    legend.position = "right",
    panel.grid.major = element_blank(),  
    panel.grid.minor = element_blank(), 
  )

Code

#% of trees that are poor, critical, dead in manhattan districts
manhattan_health_pct <- trees_with_district |>
  filter(Borough == "Manhattan") |>
  group_by(CounDist) |>
  summarise(
    num_poor_critical_dead = sum(tpcondition %in% c("Poor", "Critical", "Dead"), na.rm = TRUE),
    total_trees = n(),
    pct_poor_critical_dead = (num_poor_critical_dead / total_trees) * 100,
    .groups = "drop"
  ) |>
  arrange(desc(pct_poor_critical_dead))

manhattan_health_pct

This work ©2025 by Ghirschhorn was initially prepared as a Mini-Project for STA 9750 at Baruch College. More details about this course can be found at the course site and instructions for this assignment can be found at MP #03

--- title: "MP03 - New York City's Trees: Distribution, Diversity, and Urban Impact" author: Tova Hirschhorn date: "14 November 2025" format: html: preview: true code-fold: true code-tools: true toc: true toc-depth: 3 self-contained: true execute: warning: false message: false cache: true --- ```{r setup-libraries, include=FALSE} # Load libraries library(sf) library(dplyr) library(readr) library(ggplot2) library(scales) library(tidyverse) library(viridis) library(leaflet) library(RColorBrewer) library(httr2) ``` ## I. Executive Summary Mini-Project 03 explores New York City's extensive green spaces, encompassing over 30,000 acres of public parkland across its 51 City Council districts in the five boroughs. The project emphasizes the responsible acquisition of data from the NYC TreeMap and the NYC Department of Planning, leveraging API access, big data techniques, and geospatial analysis to ensure data integrity and reproducibility. The analysis integrates multiple spatial data sources to examine tree distribution, species diversity, and overall tree health, while making use of visualization techniques to clearly convey patterns and insights. This approach provides a deeper understanding of the environmental space around us and highlights the community value of New York's urban forest. The project also includes a government project design component, using district-level tree data to make an informed hypothetical Parks Department proposal. ## II. Data Acquisition and Preparation Two primary datasets were acquired and prepared: NYC City Council District Boundaries and NYC Tree Points. - City Council District Boundaries: The file was downloaded as a static file from the NYC Department of Planning site. The data was stored locally as a zip file, then unzipped, and read using the `st_read` function. The data was then transformed to the World Geodetic System (WGS 84) coordinate system to standardize projections and integration with other geospatial data. - NYC Tree Points: The complete NYC TreeMap dataset was obtainted from the NYC OpenData API in GeoJSON format. The data was downloaded iteratively using `$limit` and `$offset` parameters to ensure responsible API usage and the results were saved locally to prevent repeated downloads. All files were then combined into a single `sf` object using `bind_rows`. Subsetting and caching techniques were used to handle large datasets efficiently. ```{r download-city-council-data, cache = TRUE} #Task 1 - Downloading City Council Districts data #Create directory if(!dir.exists(file.path("data", "mp03"))){ dir.create(file.path("data", "mp03"), showWarnings=FALSE, recursive=TRUE) } #Define paths and URL NYC_COUNCIL_ZIP <- file.path("data", "mp03", "nycc_25c.zip") NYC_COUNCIL_URL <- "https://s-media.nyc.gov/agencies/dcp/assets/files/zip/data-tools/bytes/city-council/nycc_25c.zip" #Download ZIP only if file not not exist if(!file.exists(NYC_COUNCIL_ZIP)) { download.file(NYC_COUNCIL_URL, destfile = NYC_COUNCIL_ZIP, mode = "wb") message("Downloaded NYC City Council Districts ZIP file.") } else { message("ZIP file already exists; skipping download.") } #Define shapefile path NYC_COUNCIL_SHP <- file.path("data", "mp03", "nycc.shp") #Unzip file only if shapefile does not exist if (!file.exists(NYC_COUNCIL_SHP)) { unzip(NYC_COUNCIL_ZIP, exdir = "data/mp03") message("Unzipped shapefile.") } else { message("Shapefile already exists; skipping unzip.") } #Correct path to shapefile NYC_COUNCIL_SHP <- "data/mp03/nycc_25c/nycc.shp" #Read shapefile council_districts <- st_read("data/mp03/nycc_25c/nycc.shp", quiet = TRUE) #Check first few rows #head(council_districts) #Transform to WGS84 council_districts <- st_transform(council_districts, crs = "WGS84") #Simply geometry council_districts <- council_districts |> mutate(geometry = st_simplify(geometry, dTolerance = 10)) ``` ```{r task2-download-trees, cache = TRUE} #Task 2 - Downloading NYC Open Data Forestry Tree Points data #Downloading Tree Points using API #Create folder to store data if it doesn't exist if(!dir.exists("data/mp03")) dir.create("data/mp03", recursive = TRUE) #Define API endpoint and file paths base_url <- "https://data.cityofnewyork.us/resource/hn5i-inap.geojson" limit <- 50000 #number of rows per request offset <- 0 #start from the beginning page <-1 #page counter #List to store each page all_data<-list() #Loop to download all pages repeat { file_path <- file.path("data/mp03", paste0("trees_", page, ".geojson")) if(!file.exists(file_path)) { # Build request with limit and offset req <- request(base_url) |> req_url_query(`$limit` = limit, `$offset` = offset) #Perform the request resp <-req_perform(req) #Save raw response to a file writeBin(resp$body, file_path) message("Downloaded page ", page) } else { message("File already exists: page ", page, "; skipping download") } #Read the downloaded GeoJSON page_data <- st_read(file_path, quiet=TRUE) all_data[[page]] <- page_data # If fewer rows returned than limit, we reached the end if(nrow(page_data) < limit) break #Increment for next page page<- page+1 offset <- offset + limit } #Combine all pages into a single sf object nyc_trees <- bind_rows(all_data) #read the first page of tree points nyc_trees_page1 <- st_read("data/mp03/trees_1.geojson", quiet = TRUE) #Creating a smaller sample for plotting #set.seed(123) # reproducibility #nyc_trees_sample <- nyc_trees_page1 |> slice_sample(n = 500000) ``` ## III. Exploring New York City's Urban Tree Environment New York City, divided into 51 City Council Districts, is home to over one million trees. As the map below shows, even in a city defined by towering skyscrapers, greenery plays a vital role, weaving pockets of nature throughout the urban landscape. ```{r, cache = TRUE} #Task 3 - Mapping NYC trees ggplot()+ #Creating a ggplot for Council Districts geom_sf(data=council_districts, fill="gray95", color= "black", size=6) + #Create ggplot for NYC Trees geom_sf(data=nyc_trees, color="forestgreen", alpha=0.01, size=0.2) + theme_minimal() + labs( title = "New York City Trees by City Council District", subtitle= paste(scales::comma(nrow(nyc_trees)), "Trees Across 51 Districts"), caption = "Data: NYC Open Data and NYC Planning" ) + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), # center & bold title plot.subtitle = element_text(hjust = 0.5, size = 12), # center subtitle plot.caption = element_text(size = 8), plot.margin = margin(10, 10, 10, 10) ) ``` ```{r, cache = TRUE, results='hide'} #Task 4 - Creating District-Level joins for analysis of tree coverage #Assigning each tree to the district that contains it trees_with_district <- st_join( nyc_trees, council_districts, join=st_intersects ) #Check first few rows #head(trees_with_district) ``` Q1. Council City District 51, which covers the South Shore of Staten Island, has the most trees in the city. The district features numerous parks, including Great Kills Park, Blue Heron Park, Wolfe's Pond Park, Long Pond Park. It is also home to Freshkills Park, currently under development on top of a former landfill. Once completed, Freshkills Park will cover 2,200 acres, making it the largest park created in New York City since the 19th Century. ```{r, cache=TRUE} #Task 4. Q1 - Determine which council district has the most trees trees_by_district <- trees_with_district |> st_set_geometry(NULL) |> group_by(CounDist)|> summarise(num_trees = n())|> arrange(desc(num_trees)) #Select top 10 districts by number of trees top_trees <- trees_by_district |> slice_max(num_trees, n=10) #Create bar chart ggplot(top_trees, aes(x = reorder(CounDist, -num_trees), y = num_trees)) + geom_col(fill = "forestgreen") + labs( title = "Top 10 NYC Council Districts by Number of Trees", x = "Council District", y = "Number of Trees" ) + theme_minimal() + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 10), plot.title = element_text(hjust = 0.5, face = "bold") ) + scale_y_continuous(labels = scales::comma) ``` Q2. New York City's 7th City Council district is relatively small compared to other districts, covering roughly 5.5 km^2^ of land. Despite its size, it has the highest tree density in the city, with approximately 2.8 trees per hectare. This district covers several small neighborhoods in upper Manhattan, such as Hamilton Heights, Morningside Heights, Manhattanville, and Manhattan Valley. It also includes parts of Washington Heights and the Upper West Side. ```{r, cache=TRUE, results='hide'} #Task 4. Q2 - Determine which council district has the highest density of trees. #Count trees per district tree_counts <- trees_with_district|> group_by(CounDist, Shape_Area) |> summarise(tree_count = n(), .groups = "drop") #Compute tree density tree_counts <- tree_counts |> mutate(trees_density = tree_count/Shape_Area) #Find the district with the highest density highest_density <- tree_counts |> filter(trees_density == max(trees_density, na.rm = TRUE)) highest_density ``` Q3. The choropleth map below highlights the New York City districts with the highest fractions of dead trees. District 32, covering neighborhoods such as Howard Beach, Ozone Park, and the Rockaways shows the most severe conditions, with 14.5% of its trees classified as dead. Other highly affected districts are concentrated in neighborhoods on Staten Island, other parts of Queens, and Brooklyn, where dead-tree rates exceed 14%. ```{r, cache=TRUE, echo=TRUE, message=FALSE, warning=FALSE, results='hide'} #Task 4. Q3 - Determine which council district has the highest fraction of dead trees out of all trees. library(sf) library(dplyr) library(tidyverse) library(scales) #Filter out uknown and NA trees trees_clean <- trees_with_district|> filter(!is.na(tpcondition) & tpcondition != "Unknown") #Compute fraction of dead trees per district dead_tree_fraction <- trees_clean|> st_set_geometry(NULL)|> group_by(CounDist) |> summarise( total_trees = n(), dead_trees = sum(tpcondition == "Dead"), dead_fraction = dead_trees/total_trees )|> arrange(desc(dead_fraction)) #Joining fraction back to district geomtries to create choropleth map districts_with_fraction <- council_districts|> left_join(dead_tree_fraction, by = "CounDist") ggplot(districts_with_fraction) + geom_sf(aes(fill = dead_fraction), color = "gray40", size = 0.5) + scale_fill_viridis_c( option = "C", direction = -1, trans = "sqrt", # exaggerates small differences labels = scales::percent_format(accuracy = 1) ) + labs( title = "Fraction of Dead Trees by Council District", fill = "Dead Tree Fraction", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), plot.caption = element_text(size = 8), plot.background = element_rect(fill = "white", color = NA), panel.background = element_rect(fill = "white", color = NA), panel.grid.major = element_line(color = "gray90", size = 0.3), panel.grid.minor = element_line(color = "gray95", size = 0.2), plot.margin = margin(10, 10, 10, 10) ) ``` Q4. Manhattan is home to a diverse array of tree species across the borough. The chart below highlights the top five species in Manhattan, with the Thornless Honeylocust being the most common. This fast-growing tree tolerates pollution and adapts to various soil types. It thrives in the sun and in the Fall, its leaves turn yellow and often shrivel away, which reduces the need for raking. ```{r, cache=TRUE} #Task 4. Q4 - Determine what is the most common tree species in Manhattan #Adding a borough column trees_with_district <- trees_with_district |> mutate( Borough = case_when( CounDist >= 1 & CounDist <= 10 ~ "Manhattan", CounDist >= 11 & CounDist <= 18 ~ "Bronx", CounDist >= 19 & CounDist <= 32 ~ "Queens", CounDist >= 33 & CounDist <= 48 ~ "Brooklyn", CounDist >= 49 & CounDist <= 51 ~ "Staten Island", TRUE ~ NA_character_ ) ) #Filter for Manhattan manhattan_trees <- trees_with_district |> filter(Borough == "Manhattan") #Count tree species and find the most common for Manhattan most_common_manhattan <- manhattan_trees |> st_set_geometry(NULL) |> # remove geometry for speed group_by(genusspecies) |> summarise(num_trees = n(), .groups = "drop") |> arrange(desc(num_trees)) |> slice(1) # top species #Filter top 10 species in Manhattan top_species <- manhattan_trees |> st_set_geometry(NULL) |> group_by(genusspecies) |> summarise(num_trees = n(), .groups = "drop") |> arrange(desc(num_trees)) |> slice_head(n = 5) # Wrap species names to ~20 characters per line top_species <- top_species|> mutate(genusspecies_wrapped = str_wrap(genusspecies, width = 20)) #Plotting the results ggplot(top_species, aes(x = reorder(genusspecies_wrapped, num_trees), y = num_trees, fill = num_trees)) + geom_col(width = 0.7, show.legend = TRUE) + coord_flip() + scale_y_continuous(labels = scales::comma, expand = c(0,0)) + scale_fill_gradient( low = "lightgreen", high = "darkgreen", labels = scales::comma # add commas to legend labels ) + labs( title = "Manhattan's 5 Most Common Tree Species", x = NULL, y = "Number of Trees", fill = "Number of Trees", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), plot.caption = element_text(size = 8, hjust = 1), axis.text.y = element_text(size = 9), axis.text.x = element_text(size = 9), axis.title.x = element_text(size = 10), legend.title = element_text(size = 10), panel.grid.major = element_blank(), # remove all major grids panel.grid.minor = element_blank(), # remove all minor grids plot.margin = margin(t = 10, r = 10, b = 10, l = 60) ) ``` Q5. Since the Thornless Honeylocust is the most common tree species in Manhattan, it is no surprise that it would be the closest tree to Baruch College. This species is widely used in urban and suburban landscaping, often planted along streets and in parking lots due to its remarkable adaptability to urban stress. ```{r, cache=TRUE, results='hide'} #Task 4. Q5 - Determine the tree species closest to Baruch's campus #Creating Baruch point - coordinate lat=40.7394, lon=-73.9833 baruch_point <- st_sfc(st_point(c(-73.9833, 40.7394)), crs = 4326) # Project to NY State Plane (feet) trees_proj <- st_transform(trees_with_district, 2263) baruch_proj <- st_transform(baruch_point, 2263) # Compute distances in feet trees_proj <- trees_proj |> mutate(distance_to_baruch_ft = as.numeric(st_distance(geometry, baruch_proj))) # Find the closest tree closest_tree <- trees_proj |> arrange(distance_to_baruch_ft) |> slice(1) closest_tree$genusspecies closest_tree$distance_to_baruch_ft ``` ```{r, echo=FALSE} #Task 4. Q5 - Map the closest tree to Baruch baruch_point <- st_sfc(st_point(c(-73.9833, 40.7394)), crs = 4326) closest_tree <- st_transform(closest_tree, 4326) # Creating interactive map leaflet() |> addProviderTiles(leaflet::providers$CartoDB.Positron) |> # Baruch College marker addMarkers( data = baruch_point, label = "Baruch College", popup = "Baruch College" )|> # Closest tree as a dot addCircleMarkers( data = closest_tree, radius = 8, color = "darkgreen", fillColor = "darkgreen", fillOpacity = 1, label = closest_tree$genusspecies, popup = paste0("Closest tree Species: ", closest_tree$genusspecies) )|> # Center and zoom near Baruch setView(lng = -73.9833, lat = 40.7394, zoom = 17) ``` ## IV. Government Project Design New York City Council District 1 encompasses several diverse and historically significant neighborhoods in Lower Manhattan, including the Financial District, Battery Park City, Chinatown, Tribeca, SoHo, and the Lower East Side. The district has experienced rapid development and land-use changes in recent years. The city's rezoning efforts for new affordable housing has also placed pressure on existing green spaces. The latest debate on the future of the Elizabeth Street Garden highlights community concerns about the loss of accessible, high-quality green space. These changes underscores the urgent need to protect and expand the district's urban forest. To preserve environmental quality, improve public health, and prevent further loss of green areas, this proposal establishes the *District 1 Tree Restoration and Expansion Initiative*. This plan aims to strengthen the district's urban forestry by improving tree health, expanding tree coverage, and strengthening the district's green infrastructure. ```{r zoomed-map-d1, echo=FALSE, fig.width=7, fig.height=7} # Filter District 1 geometry district1_geom <- council_districts |> filter(CounDist == 1) # Filter trees in District 1 trees_d1 <- trees_with_district |> filter(CounDist == 1) # Zoomed-in map ggplot() + geom_sf( data = district1_geom, fill = "gray95", color = "black", linewidth = 0.4 ) + geom_sf( data = trees_d1, color = "darkgreen", size = 0.6, alpha = 0.6 ) + labs( title = "A Close-Up of Lower Manhattan", subtitle = "Street Trees of District 1", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold", hjust = 0.5), plot.subtitle = element_text(size = 12, hjust = 0.5) ) ``` This initiative focuses on strengthening the district's urban forest by: 1. Replacing 500 unhealthy trees currently classified as "poor", "critical", or "dead". 2. Planting 1000 new trees to expand coverage, increase the district's biodiversity, and improve air quality. 3. Enhancing maintenance of tree health through regular pruning and watering, and targeted care for trees in unhealthy condition. 4. Promoting community engagement through monthly tree-related educational and recreational events to build environmental awareness. New York City's District 1 envisions a greener, more resilient landscape, where all residents have equitable access to high-quality green space. It aims to create an urban refuge that supports biodiversity, improves quality of life, and provides relief from the intensity of city living. The following analysis provides the foundation for the *District 1 Tree Restoration and Expansion Initiative*, supporting the targeted needs in tree replacement, planting and maintenance. TREE POPULATION SUMMARY District 1 is home to 12,268 trees, placing it near the middle of the distribution among other Manhattan districts. Although it has more trees than districts with the fewest trees, such as District 5, which spans New York's Upper East Side, Roosevelt Island, and a small part of East Harlem, its tree density is lower than some of the other higher-ranking districts. ```{r, cache=TRUE, results='hide'} #How many trees in D1? trees_in_d1 <- trees_with_district |> filter(CounDist == 1) |> nrow() trees_in_d1 #Count trees by district on for Manhattan trees_manhattan_counts <- trees_with_district|> filter(Borough=="Manhattan")|> st_set_geometry(NULL)|> count(CounDist, name = "num_trees")|> arrange(desc(num_trees)) trees_manhattan_counts trees_manhattan_counts <- trees_manhattan_counts %>% mutate( highlight = ifelse(CounDist == 1, "District 1", "Other Districts"), CounDist = factor(CounDist) # make it a factor for plotting ) # Plot ggplot(trees_manhattan_counts, aes(x = reorder(CounDist, num_trees), y = num_trees, fill = highlight)) + geom_col() + coord_flip() + scale_y_continuous(labels = scales::comma) + scale_fill_manual(values = c("District 1" = "darkgreen", "Other Districts" = "lightgreen")) + labs( title = "Number of Street Trees by Manhattan Council District", x = "Council District", y = "Number of Trees", fill = "", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal() + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 8), legend.position = "none", panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) ``` TREE DENSITY AND DISTRIBUTION Compared to other Manhattan districts, District 1 is the third largest area, covering nearly 7.23 km^2^ of land. Despite its size, its tree density is among the lowest in the borough, with less than 2 trees per hectare. This highlights the pressing need to expand green space and increase tree coverage in the district. ```{r cache=TRUE, results='hide'} #Calculate tree density for more accurate comparison of number of trees per unit area #Calculate land area for all districts in Manhattan manhattan_districts_area <- council_districts |> filter(CounDist >= 1 & CounDist <= 10) |> mutate( area_sqm = st_area(geometry), area_km2 = as.numeric(area_sqm) / 1e6 ) |> select(CounDist, area_sqm, area_km2)|> arrange(desc(area_km2)) #manhattan_districts_area # Filter for Manhattan manhattan_trees <- trees_with_district |> filter(Borough == "Manhattan") # Count tree density per district trees_manhattan_density <- manhattan_trees |> st_set_geometry(NULL) |> # remove geometry for faster processing group_by(CounDist, Shape_Area) |> summarise(num_trees = n(), .groups = "drop") |> mutate(tree_density_per_sqm = num_trees / Shape_Area, # trees per m^2 tree_density_per_hectare = tree_density_per_sqm * 10000) |> # trees per hectare arrange(desc(tree_density_per_hectare)) # optional: sort by density # View the results trees_manhattan_density #Create visual plot of tree density in Manhattan manhattan_districts <- council_districts |> filter(CounDist >= 1 & CounDist <= 10) # Compute number of trees per district trees_manhattan <- trees_with_district |> filter(Borough == "Manhattan") |> st_set_geometry(NULL) |> group_by(CounDist) |> summarise(num_trees = n(), .groups = "drop") #Convert both to integer manhattan_districts <- manhattan_districts |> mutate(CounDist = as.integer(CounDist)) trees_manhattan_counts <- trees_manhattan_counts |> mutate(CounDist = as.integer(CounDist)) # Combine tree counts with district geometries manhattan_districts <- manhattan_districts |> left_join(trees_manhattan_counts, by = "CounDist") |> mutate( tree_density_per_sqm = num_trees / Shape_Area, tree_density_per_hectare = tree_density_per_sqm * 10000 ) # Plot choropleth map ggplot(manhattan_districts) + geom_sf(aes(fill = tree_density_per_hectare), color = "black", size = 0.3) + geom_sf_text(aes(label = CounDist), size = 4, color = "white") + scale_fill_viridis( option = "J", direction = -1, name = "Trees per hectare" ) + labs( title = "Tree Density by Manhattan City Council District", subtitle = "Number of trees per hectare of total district area", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), plot.subtitle = element_text(hjust = 0.5, size = 12), plot.caption = element_text(size = 8, hjust = 1), plot.margin = margin(20, 20, 20, 20), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), plot.background = element_blank(), axis.text = element_blank(), # remove axis text axis.ticks = element_blank(), # remove ticks axis.title = element_blank() # remove axis titles ) ``` TREE HEALTH PROFILE The tree health in District 1 is of significant concern. Over 15% of its trees are classified as being in "poor", "critical", or "dead" condition, making it the third highest district in Manhattan with unhealthy trees. Although District 10 has the highest percentage (18%) of trees in poor condition, it also has a substantially larger total number of trees. In contrast, District 1's combination of lower tree density coupled with a relatively high proportion of unhealthy trees highlights the urgency for targeted care, maintenance, and other efforts to improve the health of the district's urban forest. ```{r cache=TRUE, results='hide'} #Define condition order from best to worst tp_levels <- c("Excellent", "Good", "Fair", "Poor", "Critical", "Dead") #Filter out unknown and NA conditions and prepare data in ordered factor trees_health_manhattan <- manhattan_trees |> filter(!is.na(tpcondition) & tpcondition != "Unknown") |> st_set_geometry(NULL) |> mutate(tpcondition = factor(tpcondition, levels = tp_levels, ordered = TRUE), highlight = if_else(CounDist == 1, "District 1", "Other Districts")) |> group_by(CounDist, tpcondition) |> summarise(num_trees = n(), .groups = "drop") # Plot stacked bar chart for all tpcondition ggplot(trees_health_manhattan, aes(x = factor(CounDist), y = num_trees, fill = tpcondition)) + geom_bar(stat = "identity") + scale_fill_brewer(palette = "Greens") + labs( title = "Tree Health by Manhattan City Council District", x = "Manhattan Council District", y = "Number of Trees", fill = "Tree Condition", caption = "Data: NYC Open Data and NYC Planning" ) + theme_minimal() + theme( plot.title = element_text(hjust = 0.5, face = "bold", size = 14), axis.text.x = element_text(size = 8), legend.position = "right", panel.grid.major = element_blank(), panel.grid.minor = element_blank(), ) #% of trees that are poor, critical, dead in manhattan districts manhattan_health_pct <- trees_with_district |> filter(Borough == "Manhattan") |> group_by(CounDist) |> summarise( num_poor_critical_dead = sum(tpcondition %in% c("Poor", "Critical", "Dead"), na.rm = TRUE), total_trees = n(), pct_poor_critical_dead = (num_poor_critical_dead / total_trees) * 100, .groups = "drop" ) |> arrange(desc(pct_poor_critical_dead)) manhattan_health_pct ``` ```{r, cache=TRUE,echo=FALSE, results='hide'} #Number of unique sepecies in D1 vs. other districts trees_with_district |> filter(Borough == "Manhattan")|> group_by(CounDist)|> summarise( n_species = n_distinct(genusspecies), .groups = "drop" ) |> arrange(desc(n_species)) #Species balance among districts in Manhattan # Filter District 1 trees d1_trees <- trees_with_district |> filter(CounDist == 1, !is.na(genusspecies) & genusspecies != "Unknown") # Count trees per species d1_species_count <- d1_trees |> group_by(genusspecies) |> summarise(count = n()) |> arrange(desc(count)) d1_species_count ``` ------------------------------------------------------------------------ This work ©2025 by Ghirschhorn was initially prepared as a Mini-Project for STA 9750 at Baruch College. More details about this course can be found at [the course site](https://michael-weylandt.com/STA9750) and instructions for this assignment can be found at [MP #03](https://michael-weylandt.com/STA9750/miniprojects/mini03.html)